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Automatic differentiation in machine learning: a survey
Baydin, Atilim Gunes; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Automatic differentiation (AD) is a technique for calculating derivatives of numeric functions expressed as computer programs efficiently and accurately, used in fields such as computational fluid dynamics, nuclear engineering, and atmospheric sciences. Despite its advantages and use in other fields, machine learning practitioners have been little influenced by AD and make scant use of available tools. We survey the intersection of AD and machine learning, cover applications where AD has the potential to make a big impact, and report on some recent developments in the adoption of this technique. We aim to dispel some misconceptions that we contend have impeded the use of AD within the machine learning community.
Keyword(s): Optimization; Gradient methods; Backpropagation
Publication Date:
Type: Report
Peer-Reviewed: No
Institution: Maynooth University
Citation(s): Baydin, Atilim Gunes and Pearlmutter, Barak A. and Radul, Alexey Andreyevich and Siskind, Jeffrey Mark (2015) Automatic differentiation in machine learning: a survey. Working Paper. arXiv.
Publisher(s): arXiv
File Format(s): other
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First Indexed: 2020-04-02 06:32:13 Last Updated: 2020-04-02 06:32:13